GAMES Webinar 2017-03期 | Deep 3D Representation Learning for Visual Computing-UCSD-苏昊

【GAMES Webinar 2017-03期】

报告嘉宾:Hao Su (苏昊),UCSD (加州大学圣地亚哥分校)

报告时间:2017年7月6日(星期四)晚20:00-21:30(北京时间)

主持人:李扬彦,山东大学(个人主页: http://www.yangyan.li/

报告题目:Deep 3D Representation Learning for Visual Computing

报告摘要:

Among all digital representations we have for real physical objects, 3D is arguably the most expressive encoding. 3D representations allow storage and manipulation of high-level information (e.g. semantics, affordances, function) as well as low-level features (e.g. appearance, materials) about the object. Exploiting this 3D structure promises to improve our ability to build machines and autonomous agents that sense, understand, and act on the physical world around us. Historically, 3D visual computing has predominantly focused on single 3D models or small model collections. Now, however, with the advent of large 3D repositories of object models and inexpensive 3D scanning, the opportunity arises to re-define the field from the perspective of 3D big data.

In this talk, I will overview the recent progress on deep learning methods for analyzing and synthesizing 3D data and introduce my work on this topic. Different from 2D images that have a dominant representation as arrays, 3D geometric data have multiple popular representations, ranging from point cloud, meshes, volumetric field to multi-view images, each fitting their own application scenarios. From a research point of view, each type of data format has its own properties that pose challenges to deep architecture design while also provide the opportunity for novel and efficient solutions. Under the guiding principle of learning representations from 3D big data, these approaches have led to novel learning architectures resulting in top-performing algorithms. I will conclude my talk by describing several promising directions for future research.

讲者简介:

Hao Su has been in UC San Diego as Assistant Professor of Computer Science and Engineering since July, 2017. He is affiliated with the Contextual Robotis Institute and Center for Visual Computing. He served in the program committee of multiple conferences and workshops on computer vision, computer graphics, and machine learning. He is the program chair of 3DV’17, publication chair of 3DV’16, and chair of various workshops at CVPR, ECCV, and ICCV. He is also an invited speaker at NIPS’16 workshop, 3DV’16 workshop, and CVPR’17 tutorial on 3D deep learning.

Professor Su is interested in fundamental problems in broad disciplines related to artificial intelligence, including machine learning, computer vision, computer graphics, and robotics. He is particularly interested in deep learning for 3D data understanding and interconnecting 3D data with other modalities such as images and texts. Professor Su is leading the construction of ShapeNet, a large-scale 3D-centric knowledge base of objects, and used to work on ImageNet, a large-scale 2D image database. Applications of my research include robotics, autonomous driving, virtual/augmented reality, smart manufacturing, etc. He has published numerous papers in top conferences and journals in computer vision, computer graphics, machine learning, and social networks.

Liu, Ligang

刘利刚,中国科学技术大学教授、博导。分别于1996年及2001年于浙江大学获得应用数学学士及博士学位。从事计算机图形学研究,已在该领域顶级期刊ACM Transactions on Graphics上发表论文三十余篇。曾于微软亚洲研究院、浙江大学、哈佛大学工作或访问。曾获微软青年教授奖、陆增镛CAD&CG高科技奖一等奖、国家自然科学奖二等奖等奖项。任多个国内外会议的大会共同主席或论文共同主席及多个国际学术期刊编委。负责创建了中科大《计算机图形学前沿》暑期课程及CCF CAD&CG专委图形学在线交流平台GAMES。

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